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import time
from typing import TYPE_CHECKING, Optional, Sequence, Set
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.sagemaker import LogState, SageMakerHook
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class SageMakerBaseSensor(BaseSensorOperator):
"""
Contains general sensor behavior for SageMaker.
Subclasses should implement get_sagemaker_response() and state_from_response() methods.
Subclasses should also implement NON_TERMINAL_STATES and FAILED_STATE methods.
"""
def __init__(self, *, aws_conn_id: str = 'aws_default', **kwargs):
super().__init__(**kwargs)
self.aws_conn_id = aws_conn_id
self.hook: Optional[SageMakerHook] = None
[docs] def get_hook(self) -> SageMakerHook:
"""Get SageMakerHook."""
if self.hook:
return self.hook
self.hook = SageMakerHook(aws_conn_id=self.aws_conn_id)
return self.hook
[docs] def poke(self, context: 'Context'):
response = self.get_sagemaker_response()
if not (response['ResponseMetadata']['HTTPStatusCode'] == 200):
self.log.info('Bad HTTP response: %s', response)
return False
state = self.state_from_response(response)
self.log.info('Job currently %s', state)
if state in self.non_terminal_states():
return False
if state in self.failed_states():
failed_reason = self.get_failed_reason_from_response(response)
raise AirflowException(f'Sagemaker job failed for the following reason: {failed_reason}')
return True
[docs] def non_terminal_states(self) -> Set[str]:
"""Placeholder for returning states with should not terminate."""
raise NotImplementedError('Please implement non_terminal_states() in subclass')
[docs] def failed_states(self) -> Set[str]:
"""Placeholder for returning states with are considered failed."""
raise NotImplementedError('Please implement failed_states() in subclass')
[docs] def get_sagemaker_response(self) -> dict:
"""Placeholder for checking status of a SageMaker task."""
raise NotImplementedError('Please implement get_sagemaker_response() in subclass')
[docs] def get_failed_reason_from_response(self, response: dict) -> str:
"""Placeholder for extracting the reason for failure from an AWS response."""
return 'Unknown'
[docs] def state_from_response(self, response: dict) -> str:
"""Placeholder for extracting the state from an AWS response."""
raise NotImplementedError('Please implement state_from_response() in subclass')
[docs]class SageMakerEndpointSensor(SageMakerBaseSensor):
"""
Polls the endpoint state until it reaches a terminal state. Raises an
AirflowException with the failure reason if a failed state is reached.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:SageMakerEndpointSensor`
:param endpoint_name: Name of the endpoint instance to watch.
"""
[docs] template_fields: Sequence[str] = ('endpoint_name',)
[docs] template_ext: Sequence[str] = ()
def __init__(self, *, endpoint_name, **kwargs):
super().__init__(**kwargs)
self.endpoint_name = endpoint_name
[docs] def non_terminal_states(self):
return SageMakerHook.endpoint_non_terminal_states
[docs] def failed_states(self):
return SageMakerHook.failed_states
[docs] def get_sagemaker_response(self):
self.log.info('Poking Sagemaker Endpoint %s', self.endpoint_name)
return self.get_hook().describe_endpoint(self.endpoint_name)
[docs] def get_failed_reason_from_response(self, response):
return response['FailureReason']
[docs] def state_from_response(self, response):
return response['EndpointStatus']
[docs]class SageMakerTuningSensor(SageMakerBaseSensor):
"""
Asks for the state of the tuning state until it reaches a terminal state.
Raises an AirflowException with the failure reason if a failed state is reached.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:SageMakerTuningSensor`
:param job_name: Name of the tuning instance to watch.
"""
[docs] template_fields: Sequence[str] = ('job_name',)
[docs] template_ext: Sequence[str] = ()
def __init__(self, *, job_name: str, **kwargs):
super().__init__(**kwargs)
self.job_name = job_name
[docs] def non_terminal_states(self):
return SageMakerHook.non_terminal_states
[docs] def failed_states(self):
return SageMakerHook.failed_states
[docs] def get_sagemaker_response(self):
self.log.info('Poking Sagemaker Tuning Job %s', self.job_name)
return self.get_hook().describe_tuning_job(self.job_name)
[docs] def get_failed_reason_from_response(self, response):
return response['FailureReason']
[docs] def state_from_response(self, response):
return response['HyperParameterTuningJobStatus']
[docs]class SageMakerTrainingSensor(SageMakerBaseSensor):
"""
Polls the training job until it reaches a terminal state. Raises an
AirflowException with the failure reason if a failed state is reached.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:SageMakerTrainingSensor`
:param job_name: Name of the training job to watch.
:param print_log: Prints the cloudwatch log if True; Defaults to True.
"""
[docs] template_fields: Sequence[str] = ('job_name',)
[docs] template_ext: Sequence[str] = ()
def __init__(self, *, job_name, print_log=True, **kwargs):
super().__init__(**kwargs)
self.job_name = job_name
self.print_log = print_log
self.positions = {}
self.stream_names = []
self.instance_count: Optional[int] = None
self.state: Optional[int] = None
self.last_description = None
self.last_describe_job_call = None
self.log_resource_inited = False
[docs] def init_log_resource(self, hook: SageMakerHook) -> None:
"""Set tailing LogState for associated training job."""
description = hook.describe_training_job(self.job_name)
self.instance_count = description['ResourceConfig']['InstanceCount']
status = description['TrainingJobStatus']
job_already_completed = status not in self.non_terminal_states()
self.state = LogState.TAILING if (not job_already_completed) else LogState.COMPLETE
self.last_description = description
self.last_describe_job_call = time.monotonic()
self.log_resource_inited = True
[docs] def non_terminal_states(self):
return SageMakerHook.non_terminal_states
[docs] def failed_states(self):
return SageMakerHook.failed_states
[docs] def get_sagemaker_response(self):
if self.print_log:
if not self.log_resource_inited:
self.init_log_resource(self.get_hook())
(
self.state,
self.last_description,
self.last_describe_job_call,
) = self.get_hook().describe_training_job_with_log(
self.job_name,
self.positions,
self.stream_names,
self.instance_count,
self.state,
self.last_description,
self.last_describe_job_call,
)
else:
self.last_description = self.get_hook().describe_training_job(self.job_name)
status = self.state_from_response(self.last_description)
if (status not in self.non_terminal_states()) and (status not in self.failed_states()):
billable_time = (
self.last_description['TrainingEndTime'] - self.last_description['TrainingStartTime']
) * self.last_description['ResourceConfig']['InstanceCount']
self.log.info('Billable seconds: %s', (int(billable_time.total_seconds()) + 1))
return self.last_description
[docs] def get_failed_reason_from_response(self, response):
return response['FailureReason']
[docs] def state_from_response(self, response):
return response['TrainingJobStatus']
